{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Demonstration of Lorenz Coefficient for Quantifying Spatial, Subsurface Heterogeneity\n",
    "\n",
    "#### Alan Scherman, Rice University, UT PGE 2020 SURI\n",
    "\n",
    "#### Supervised and Updated Plots by:\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, The University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "***\n",
    "#### Introduction\n",
    "\n",
    "This is a demonstration of how to calculate the Lorenz Coefficient of a subsurface sample from well log porosity and permeability measurements. The Lorenz Coefficient is an important heuristic imported form the sphere of macroeconomics used to quantify the spatial heterogeneity of a subsurface sample. The Lorenz Coefficient is obtained by doubling the area between the Lorenz Curve and the Homogeneous Line. It ranges from 0 to 1 where, by convention, a coefficient of less than 0.3 suggests low heterogeneity and a coefficient of more than 0.6 indicates high heterogeneity. In practical terms, a low spatial heterogeneity allows for simple displacement of subsurface fluids and a high recovery factor.\n",
    "***\n",
    "#### Objective\n",
    "\n",
    "To understand and apply the methodology to calculate the Lorenz Curve and Coefficient from porosity and permeability measurements through Python functionalities.\n",
    "\n",
    "***\n",
    "#### Calculation procedure\n",
    "\n",
    "The following list contains the necessary steps to determine the Lorenz Coefficient of a subsurface sample:\n",
    "\n",
    "**(1)** Sort porosity ($\\phi$) and permeability (**_k_**) in descending order of ratio **_k_**/$\\phi$;\n",
    "\n",
    "**(2)** Calculate storage (**_S.C._**) and flow capacity (**_F.C._**) of each layer (region between depth measurements) with:\n",
    "\n",
    "<br>\n",
    "\\begin{equation}\n",
    "S.C. = \\phi*h\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "F.C. = k*h\n",
    "\\end{equation}\n",
    "\n",
    "where **_h_** is the layer thickness;\n",
    "\n",
    "**(3)** Calculate the _cumulative_ storage (**_C.S.C._**) and flow capacities (**_C.F.C._**) of each layer with:\n",
    "\n",
    "<br>\n",
    "\\begin{equation}\n",
    "C.S.C. = \\sum^{current layer}_{i = 1} \\phi*h\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "C.F.C. = \\sum^{current layer}_{i = 1} k*h\n",
    "\\end{equation}\n",
    "\n",
    "**(4)** Normalize the cumulative storage and flow capacities by dividing them by the largest cumulative storage and flow capacities (i.e. the last cumulative capacities calculated), respectively;\n",
    "\n",
    "**(5)** _(Optional)_ Plot the normalized cumulative flow capacities against the normalized cumulative storage capacities (i.e. the Lorenz Curve);\n",
    "\n",
    "**(6)** Find a curve fit for the normalized capacities in the Cartesian plane (usually a 3rd degree polynomial is sufficient);\n",
    "\n",
    "**(7)** Integrate to find the area between the Lorenz Curve and the Homogenous Line;\n",
    "\n",
    "**(8)** Divide the result found in **(7)** by 0.5 to obtain the Lorenz Coefficient:\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "LorenzCoefficient = \\frac{\\int^{1}_{0} (LorenzCurve - HomogeneousLine)}{0.5}\n",
    "\\end{equation}\n",
    "***\n",
    "#### Load the required libraries\n",
    "\n",
    "The program below utilizes some standard Python packages. These should be previously installed if you have Anaconda or other similar software."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                                                  # To import data from .xlsx or .csv file\n",
    "import numpy as np                                                   # For numerical array management\n",
    "from matplotlib import pyplot as plt                                 # For graphical display of Lorenz Curve\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "plt.rc('axes', axisbelow=True)                            # set axes and grids in the background for all plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs. \n",
    "***\n",
    "#### Importing porosity and permeability data\n",
    "\n",
    "The data file for this demonstration is availbale at [here](https://github.com/GeostatsGuy/GeoDataSets/blob/master/WellPorPermSample_3.xlsx). \n",
    "\n",
    "The well log data will be imported from an Excel (.xlsx) file. There are similar Pandas methods which allow the importation of data from .csv and other common file types. First, the Excel data is stored as a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>265.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>13.6</td>\n",
       "      <td>116.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.75</td>\n",
       "      <td>9.0</td>\n",
       "      <td>136.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>17.6</td>\n",
       "      <td>216.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.25</td>\n",
       "      <td>9.4</td>\n",
       "      <td>131.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Depth   Por   Perm\n",
       "0   0.25  13.0  265.5\n",
       "1   0.50  13.6  116.9\n",
       "2   0.75   9.0  136.9\n",
       "3   1.00  17.6  216.7\n",
       "4   1.25   9.4  131.6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_excel('WellPorPermSample_3.xlsx')                # Include file directory with os.chdir() method if necessary\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/WellPorPermSample_3.csv') # load from Dr. Pyrcz's GitHub respository\n",
    "df = df.rename(columns={\"Depth (m)\":\"Depth\", \"Por (%)\":\"Por\", \"Perm (mD)\":\"Perm\"})\n",
    "n = len(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Feature Engineering\n",
    "\n",
    "##### Determine Layer Thickness\n",
    "\n",
    "The layer thickness is found by subtracting a shallower depth from a deeper depth. \n",
    "\n",
    "* For most well logs, the layers will have a uniform thickness. \n",
    "* However, irregular thicknesses are also possible and should be accounted for. \n",
    "\n",
    "##### Calculate Ratio of Permeability Divided by Porosity\n",
    "\n",
    "Calculate rock quality index by dividing permeability by porosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "      <th>Thick</th>\n",
       "      <th>PermPor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>13.0</td>\n",
       "      <td>265.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>20.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>13.6</td>\n",
       "      <td>116.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>8.595588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.75</td>\n",
       "      <td>9.0</td>\n",
       "      <td>136.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>15.211111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>17.6</td>\n",
       "      <td>216.7</td>\n",
       "      <td>0.25</td>\n",
       "      <td>12.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.25</td>\n",
       "      <td>9.4</td>\n",
       "      <td>131.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Depth   Por   Perm  Thick    PermPor\n",
       "0   0.25  13.0  265.5   0.25  20.423077\n",
       "1   0.50  13.6  116.9   0.25   8.595588\n",
       "2   0.75   9.0  136.9   0.25  15.211111\n",
       "3   1.00  17.6  216.7   0.25  12.312500\n",
       "4   1.25   9.4  131.6   0.25  14.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Thick'] = df['Depth'] - np.concatenate(([0],df['Depth'][:-1])) # shift depth and element-wise subtraction to difference\n",
    "df['PermPor'] = df['Perm']/df['Por']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Step (1) - Sort data in decreasing order of k/$\\phi$ \n",
    "\n",
    "Recall that the first step to calculate the Lorenz Coefficient is to sort all layers in descending order of their ratio of permeability by porosity. Because both the porosity and permeability values are expressed as NumPy arrays, these ratios can be easily obtained by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "      <th>Thick</th>\n",
       "      <th>PermPor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>19.75</td>\n",
       "      <td>17.2</td>\n",
       "      <td>573.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>33.343023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>26.00</td>\n",
       "      <td>9.2</td>\n",
       "      <td>263.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>28.641304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>24.50</td>\n",
       "      <td>11.0</td>\n",
       "      <td>305.1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>27.736364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>18.25</td>\n",
       "      <td>8.9</td>\n",
       "      <td>244.1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>27.426966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23.00</td>\n",
       "      <td>17.2</td>\n",
       "      <td>380.1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>22.098837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Depth   Por   Perm  Thick    PermPor\n",
       "78   19.75  17.2  573.5   0.25  33.343023\n",
       "103  26.00   9.2  263.5   0.25  28.641304\n",
       "97   24.50  11.0  305.1   0.25  27.736364\n",
       "72   18.25   8.9  244.1   0.25  27.426966\n",
       "91   23.00  17.2  380.1   0.25  22.098837"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values('PermPor',ascending = False)                   # sort in descending order\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Extract DataFrame features to NumPy arrays\n",
    "\n",
    "In order to facilitate data management and allow the use of helpful mathematical methods, we'll convert the Pandas DataFrames to NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "thick = df['Thick'].values\n",
    "por = df['Por'].values\n",
    "perm = df['Perm'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's always a good idea to check after every step in our workflows! Let's preview the resulting arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thick vector, size  105 , values: [0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25\n",
      " 0.25 0.25 0.25 0.25 0.25 0.25 0.25]\n",
      "\n",
      "Por vector  [17.2  9.2 11.   8.9 17.2  9.  13.  14.1  8.6 10.5 11.1 13.4 16.9  6.8\n",
      " 14.  13.1  7.9 10.1 10.2  9.6 15.8 17.8 11.9  8.1 15.1 13.6 17.5  8.9\n",
      " 13.3  9.5 11.8  8.4  9.1 15.7 15.   9.   5.8 12.6  9.1  7.1  4.1 15.8\n",
      "  9.5 11.4  7.6  9.4  6.2 19.4 13.7  7.1 14.8 12.5 10.4 13.8 15.4 11.7\n",
      " 17.  11.8 17.6 17.   4.4 11.8 12.6  7.2 11.9 10.1 11.7 14.1 18.5 11.9\n",
      " 11.3 13.2 10.7 10.  15.4  5.3 16.   9.9  8.  12.4 16.2 15.9  8.1  9.5\n",
      " 13.4 14.1 12.1 10.7  9.2  9.  16.6 13.6 14.4 12.7 10.  10.9  9.  11.4\n",
      " 13.3  8.9  9.2 10.3  9.1 11.4  8. ]\n",
      "\n",
      "Perm vector  [573.5 263.5 305.1 244.1 380.1 197.8 265.5 285.3 171.4 205.1 214.9 258.6\n",
      " 321.1 129.1 264.2 240.6 144.3 183.1 182.6 170.5 279.8 311.6 206.7 138.8\n",
      " 257.4 224.4 286.8 144.7 214.1 151.5 187.9 132.7 143.7 245.7 234.5 136.9\n",
      "  86.8 184.8 132.1 102.7  59.1 227.4 134.7 161.  106.4 131.6  84.9 258.4\n",
      " 179.7  92.7 190.9 160.5 133.2 176.4 193.1 146.  210.8 145.6 216.7 206.6\n",
      "  53.4 141.8 151.   85.  138.8 117.6 135.8 162.9 213.7 132.7 125.9 144.4\n",
      " 116.6 108.6 166.9  56.3 168.4 103.5  83.3 128.9 162.3 156.1  77.8  89.6\n",
      " 126.3 132.  112.3  97.7  83.9  77.5 142.7 116.9 123.3 104.   80.4  84.7\n",
      "  66.9  79.4  92.1  61.   58.8  61.9  54.3  65.6  43.5]\n"
     ]
    }
   ],
   "source": [
    "print('Thick vector, size ', len(thick), ', values:',  thick)\n",
    "print('\\nPor vector ', por)\n",
    "print('\\nPerm vector ', perm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the layer_thick array should have the same dimensions as the porosity and permeability arrays.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Step (2) - Calculate storage and flow capacities\n",
    "\n",
    "The storage and flow capacities are found by performing element-wise multiplication of the porosity and permeability arrays by the thickness array. Hence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "storage_cap = por*thick\n",
    "flow_cap = perm*thick"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Step (3) - Calculate the cumulative storage and flow capacities\n",
    "\n",
    "The next step is to compute the cumulative capacities for each layer. An easy solution to this assignment is provided by a for-loop. First, we pre-allocate the cumulative storage and flow capacities arrays. Then, we set the cumulative capacities of the first layer equal to its own capacities. Finally, we run a for-loop to compute the remaining cumlative capacities for the sequential layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumul_storage_cap = np.cumsum(storage_cap,axis=0)\n",
    "cumul_flow_cap = np.cumsum(flow_cap,axis=0)\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(n),cumul_storage_cap,c='r'); plt.xlabel('Sort Data'); \n",
    "plt.ylabel(r'Cumulative Storage Capacity ($ \\%\\cdot m$)'); plt.title('Cumulative Storage Capacity vs. Sorted Data')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(np.arange(n),cumul_flow_cap,c='r'); plt.xlabel('Sort Data'); \n",
    "plt.ylabel(r'Cumulative Flow Capacity ($ mD \\cdot m$)'); plt.title('Cumulative Flow Capacity vs. Sorted Data')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the length of the cumulative capacities arrays are one index greater than the flow and storage capacity arrays. In fact, the first entry of both arrays is set to 0 (zero) in order to ensure convergence of the future Lorenz Curve to the origin.\n",
    "\n",
    "***\n",
    "#### Step (4) - Normalize the cumulative storage and flow capacities\n",
    "\n",
    "To normalize the cumulative storage and flow capacities (i.e. limiting the range to [0,1]), we'll perform element-wise division of the cumulative storage and flow capacities by the sum of the all layers' storage and flow capacities, respectively. Thus, the normalized (also known as fractional) storage and flow capacities are given by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frac_cumul_storage_cap = cumul_storage_cap/cumul_storage_cap[-1]\n",
    "frac_cumul_flow_cap = cumul_flow_cap/cumul_flow_cap[-1]\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(n),frac_cumul_storage_cap,c='r'); plt.xlabel('Sort Data'); \n",
    "plt.ylabel(r'Fractional Cumulative Storage Capacity ($ \\%\\cdot m$)'); plt.title('Fractional Cumulative Storage Capacity vs. Sorted Data')\n",
    "plt.xlim([0,n-1]); plt.ylim([0,1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(np.arange(n),frac_cumul_flow_cap,c='r'); plt.xlabel('Sort Data'); \n",
    "plt.ylabel(r'Fractional Cumulative Flow Capacity ($ mD \\cdot m$)'); plt.title('Fractional Cumulative Flow Capacity vs. Sorted Data')\n",
    "plt.xlim([0,n-1]); plt.ylim([0,1])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Step (5) - Plot the Lorenz Curve\n",
    "\n",
    "Even though we say plot the Lorenz Curve, we'll indeed plot the fractional flow capacities against the fractional storage capacities (i.e. we'll plot some of the points on the Lorenz Curve).\n",
    "\n",
    "Let's instantiate a figure to hold our Lorenz Curve points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "plt.plot([0,1], [0,1], color = 'black', label = 'Homogeneous Line',linewidth=2)            # Homogeneous line\n",
    "plt.scatter(frac_cumul_storage_cap, frac_cumul_flow_cap, marker = 'o',s=20, \n",
    "         label = 'Experimental Lorenz Points',color = 'darkorange',edgecolor = 'black',alpha=0.8)                # Sample points of Lorenz Curve\n",
    "plt.xlim([0,1]); plt.ylim([0,1])\n",
    "plt.grid(True, which='major',linewidth = 1.0); plt.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "plt.tick_params(which='major',length=7); plt.tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "\n",
    "plt.title('Geological Heterogeneity Summarized by Lorenz Plot')\n",
    "plt.xlabel('Fractional Cumulative Storage Capacity ($\\% \\cdot m$)')\n",
    "plt.ylabel('Fractional Cumulative Flow Capacity ($mD \\cdot m$)')\n",
    "plt.legend(loc = 'lower right')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.4, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Step (6) - Find a curve fit\n",
    "\n",
    "Now that we have some of the data points of the Lorenz Curve, we can apply a curve fit to predict the behavior of this entire subsurface sample. We'll use a 3rd degree polynomial to fit the Lorenz Curve. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poly_fit =  [ 0.20055986 -0.84959872  1.63791944  0.0111195 ]\n"
     ]
    }
   ],
   "source": [
    "weights = np.ones(n)                                     \n",
    "weights[0] = 1000; weights[-1] = 1000                                           # To ensure the fit converges to (0,0) and (1,1)\n",
    "    \n",
    "poly_fit = np.polyfit(frac_cumul_storage_cap, frac_cumul_flow_cap, deg=3, w=weights)\n",
    "print('poly_fit = ', poly_fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting array contains the coefficients of the polynomial fit. To better visualize this polynomial in standard equation form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        3          2\n",
      "0.2006 x - 0.8496 x + 1.638 x + 0.01112\n"
     ]
    }
   ],
   "source": [
    "poly_fit = np.poly1d(poly_fit)\n",
    "print(poly_fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "#### Step (7) - Integrate to find the area between the Lorenz Curve and the Homogenenous LIne\n",
    "\n",
    "The advantage of having applied the _numpy.poly1d()_ method on Step (6) is that we now have a callable object for which we can evaluate it over any domain (here [0,1]). Then:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         4          3         2\n",
      "0.05014 x - 0.2832 x + 0.819 x + 0.01112 x\n"
     ]
    }
   ],
   "source": [
    "integral = np.polyint(poly_fit)\n",
    "print(integral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result above can be easily validated since the fit has polynomial behavior. Now, we can evaluate the definite integral for the area under the Lorenz Curve and then subtract the area under the homogeneous line (i.e. area of isosceles triangle of side length 1 = 0.5):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09701961267277981\n"
     ]
    }
   ],
   "source": [
    "lorenz_area = integral(1) - integral(0) - 0.5 \n",
    "print(lorenz_area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for practical purposes the integral(0) term could have been ignored since the integration constant is assumed to be 0.\n",
    "***\n",
    "#### Step (8) - Obtain the Lorenz Coefficient\n",
    "\n",
    "The final step of this procedure is to divide the _lorenz_area_ calculated on Step (7) by 0.5, which is the total area between the lines **y = 1** and **y = x** in the domain [0,1]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lorenz Coefficient = 0.194\n"
     ]
    }
   ],
   "source": [
    "lorenz_coefficient = lorenz_area/0.5\n",
    "print('Lorenz Coefficient = %.3f' %lorenz_coefficient)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the Lorenz Coefficient sits below the conventional threshold of 0.3, we can assume that the subsurface sample near the well exhibits simple displacement of fluids and a potentially high recovery factor.\n",
    "\n",
    "Let's replicate the plot from Step (5) with shaded areas to better illustrate this Lorenz Coefficient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.plot([0,1], [0,1], color = 'black', label = 'Homogeneous Line',linewidth=2)            # Homogeneous line\n",
    "plt.scatter(frac_cumul_storage_cap, frac_cumul_flow_cap, marker = 'o',s=20, \n",
    "         label = 'Experimental Lorenz Points',color = 'darkorange',edgecolor = 'black',alpha=0.8,zorder=10)                # Sample points of Lorenz Curve\n",
    "plt.xlim([0,1]); plt.ylim([0,1])\n",
    "plt.grid(True, which='major',linewidth = 1.0); plt.grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "plt.tick_params(which='major',length=7); plt.tick_params(which='minor', length=4)\n",
    "plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks\n",
    "\n",
    "plt.plot(np.linspace(0,1,100), poly_fit(np.linspace(0,1,100)),\n",
    "         linewidth = 2, label = 'Polynomial Fit', color = 'black', zorder = 10)                            # Polynomial fit\n",
    "plt.fill_between(np.linspace(0,1,100), poly_fit(np.linspace(0,1,100)),\n",
    "                 np.linspace(0,1,100), alpha = 0.5,label = 'Heterogeneous Zone',facecolor='red')                                            # Blue area\n",
    "plt.fill_between(np.linspace(0,1,100), np.linspace(0,1,100),\n",
    "                 np.zeros(100), alpha = 0.5,hatch='XX',facecolor='gray')    \n",
    "plt.plot([0,1],[0,1],linewidth = 2, color = 'black', zorder = 10)                            # Polynomial fit\n",
    "\n",
    "\n",
    "plt.title('Lorenz Curve, Emprical Points, Curve Fit and Coefficient')\n",
    "plt.xlabel('Cumulative Fraction of Storage Capacity ($m$)')\n",
    "plt.ylabel('Cumulative Fraction of Flow Capacity ($mD \\cdot m$)')\n",
    "plt.legend(loc = 'lower right',facecolor='white',framealpha = 1.0,edgecolor='black')\n",
    "plt.text(0.3, 0.8, 'Lorenz Coefficient = \\n %.3f' %lorenz_coefficient, size = 10, horizontalalignment = 'center', \\\n",
    "         bbox=dict(facecolor='white', edgecolor='black'))\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.4, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's now clear that, graphically, the Lorenz Coefficient is equivalent to the red area divided by the non-hatched area.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Conclusion\n",
    "\n",
    "This exercise detailed the procedure towards calculating the Lorenz Coefficient of a subsurface sample from its porosity and permeability measurements. We also covered some of the Python resources available to improve management of well data, perform the necessary mathematical transformations, and visualize the solution curve and the Lorenz Coefficient.\n",
    "\n",
    "If you use the GeostatsPy package, the calculation and display of the Lorenz Coefficient are available through the **lorenz_curve()** and **lorenz_display()** functions. Please check the documentation on those for more detail. \n",
    "\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n"
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